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NASA NTRS1974

Detection of moisture and moisture related phenomena from Skylab

The author had identified the following significant results. Soil moisture and precipitation variations were not detectable as tonal variations on the S19OA IR B and W photography. Some light tonal areas contained high precipitation .83 inches and high moisture content 21.1% while other light tonal areas contained only .02 inches precipitation and as little as 0.7% moisture. Similar variations were observed in dark tonal areas. This inconsistency may be caused by a lapse of 3 to 4 days from the time precipitation occurred until the photographs were taken and the fact that in the first inch of soil the measured soil moisture was generally less than 5.0%. For overall tonal contrast, the aerial color, color IR and aerial B and W appear to be the best. Cities stand out from the landscape best in the aerial color and color IR, however, to see major street patterns a combination of the two aerial B and W bands and the two IR B and W bands may be desirable. For mapping roads it is best use all 6 bands. For lake detection, the IR B and W bands would be the best but for streams the aerial B and W band would be better. The aerial color, color IR, and the two IR B and W bands are best for distinguishing cultivated and non-cultivated areas, whereas the two aerial B and W bands are better for seeing local relief. Clouds may be best seen in the aerial color and color IR bands.

Eagleman, J. R.↗
NASA NTRS2016

ESIP Information Quality Cluster (IQC)

The Information Quality Cluster (IQC) within the Federation of Earth Science Information Partners (ESIP) was initially formed in 2011 and has evolved significantly over time. The current objectives of the IQC are to: 1. Actively evaluate community data quality best practices and standards; 2. Improve capture, description, discovery, and usability of information about data quality in Earth science data products; 3. Ensure producers of data products are aware of standards and best practices for conveying data quality, and data providers distributors intermediaries establish, improve and evolve mechanisms to assist users in discovering and understanding data quality information; and 4. Consistently provide guidance to data managers and stewards on how best to implement data quality standards and best practices to ensure and improve maturity of their data products. The activities of the IQC include: 1. Identification of additional needs for consistently capturing, describing, and conveying quality information through use case studies with broad and diverse applications; 2. Establishing and providing community-wide guidance on roles and responsibilities of key players and stakeholders including users and management; 3. Prototyping of conveying quality information to users in a more consistent, transparent, and digestible manner; 4. Establishing a baseline of standards and best practices for data quality; 5. Evaluating recommendations from NASA's DQWG in a broader context and proposing possible implementations; and 6. Engaging data providers, data managers, and data user communities as resources to improve our standards and best practices. Following the principles of openness of the ESIP Federation, IQC invites all individuals interested in improving capture, description, discovery, and usability of information about data quality in Earth science data products to participate in its activities.

data products↗
NASA NTRS2019

WWAO-WSWC Workshop Report 2019 Final

EXECUTIVE SUMMARY The Western States Water Council (WSWC) and the NASA Western Water Applications Office (WWAO) hosted a joint workshop on technology transfer for water management in the Western U.S. The goals of the workshop were to understand how different agencies approach the technology transfer and research to operations (R2O) process, identify best practices, and discuss existing barriers to successful technology infusion into operational water resource management systems at the state and federal level. The workshop took place August 7-9, 2019 in Irvine, CA. Key outcomes of the meeting include the following:• U.S. Rep. Grace Napolitano provided opening remarks for the workshop, where she highlighted the critical value of water data and the importance of collaboration between state and federal agencies in working to advance the use of water data in water management, planning and policy. • A total of 33 participants (including remote participants) were part of the workshop. They included principal investigators and project teams supported by NASA (Cyanobacteria Assessment Network, Evapotranspiration for Western States, Evaporative Stress Index, the Airborne Snow Observatory, Satellite-based Snow Water Equivalent in the Sierra Nevadas, and Fallowed Area Mapping) as well as representatives from federal (USGS, NOAA, USBR, EPA) and state (CA, WY, OR, NE) agency partners. • One main outcome of the meeting was the consensus that successful transitions of new applications and new technologies into operations require careful planning, effective communication within and across institutions, resources and considerable time investments. In addition, there was broad agreement that significant lead time is often required to allow for identification of financial and technical resources to sustain operational use of new data, information and tools.• The meeting included remarks from U.S. Rep. Napolitano and discussions during presentations and breakout groups about key opportunities to develop best practices and streamline the technology transfer process. • For example, one key set of best practices that emerged revolved around the the importance of building trust and establishing clear lines of communication between the research and operational institutions. The conversations led to defining two key components of trust-building. The first aspect is purely technical. It requires effectively demonstrating that the proposed application meets the end user’s needs in terms of accuracy, format, resolution, latency, metadata and documentation. The second aspect of building trust involves developing sustained, productive and mutually-beneficial relationships with the partner operational agency. The best practices presented here span both the technical as well as the relational aspects of cultivating trust. • This workshop served as a first step in developing a broader community discussion around R2O in western water management. Many of the best practices and lessons learned described in this report represent starting places for action within the WWAO, WSWC and our colleagues’ institutions. • Effective implementation of the best practices that emerged from this workshop will require sustained investments of time, resources and transition planning. In recognition of this, the WSWC and the WWAO proposed continuation of discussions begun at the workshop through a series of semi-annual or annual workshops.

Wilardson, Tony↗
DOE OSTI2023

The gallium anomaly

In order to test the end-to-end operations of gallium solar neutrino experiments, intense electron-capture sources were fabricated to measure the responses of the radiochemical SAGE and GALLEX/GNO detectors to known fluxes of low-energy neutrinos. Such tests were viewed at the time as a cross-check, given the many tests of 71 Ge recovery and counting that had been routinely performed, with excellent results. However, the four 51 Cr and 37 Ar source experiments yielded rates below expectations, a result commonly known as the Ga anomaly. As the intensity of the electron-capture sources can be measured to high precision, the neutrino lines they produce are fixed by known atomic and nuclear rates, and the neutrino absorption cross section on 71 Ga is tightly constrained by the lifetime of 71 Ge, no simple explanation for the anomaly has been found. To check these calibration experiments, a dedicated experiment BEST was performed, utilizing a neutrino source of unprecedented intensity and a detector optimized to increase statistics while providing some information on counting rate as a function of distance from the source. The results BEST obtained are consistent with the earlier solar neutrino calibration experiments, and when combined with those measurements, yield a Ga anomaly with a significance of approximately 4𝜎, under conservative assumptions. But BEST found no evidence of distance dependence and thus no explicit indication of new physics. In this review we describe the extensive campaigns carried out by SAGE, GALLEX/GNO, and BEST to demonstrate the reliability and precision of their experimental procedures, including 71 Ge recovery, counting, and analysis. We also describe efforts to define uncertainties in the neutrino capture cross section, which now include estimates of effects at the ≲ 0.5% level such as radiative corrections and weak magnetism. With the results from BEST, an anomaly remains even if one retains only the transition to the 71 Ge ground state, whose strength is fixed by the known lifetime of 71 Ge. We then consider the new-physics solution most commonly suggested to resolve the Ga anomaly, oscillations into a sterile fourth neutrino, 𝜈 𝑒 → 𝜈 𝑠 . We find such a solution generates substantial tension with several null experiments, owing to the large mixing angle required. While this does not exclude such solutions – the sterile sector might include multiple neutrinos as well as new interactions – it shows the need for more experimental constraints, if we are to make progress in resolving the Ga and other low-energy neutrino anomalies. We conclude by consider the role future low-energy electron-capture sources could play in this effort.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Gallium neutrino absorption cross section and its uncertainty

In the recent Baksan Experiment on Sterile Transitions (BEST), a suppressed rate of neutrino absorption on a gallium target was observed, consistent with earlier results from neutrino source calibrations of the SAGE and GALLEX/GNO solar neutrino experiments. The BEST Collaboration, utilizing a 3.4 MCi 51 Cr neutrino source, found observed-to-expected counting rates at two very short baselines of 𝑅 = 0.791 ± 0.05 and 0.766 ± 0.05, respectively. Among recent neutrino experiments, BEST is notable for the simplicity of both its neutrino spectrum, line neutrinos from an electron-capture source whose intensity can be measured to a estimated precision of 0.23%, and its absorption cross section, where the precisely known rate of electron capture to the gallium ground state, 71 Ge ⁢(𝑒−, 𝜈 𝑒 )⁢ 71 Ga ⁢(g.s.), establishes a minimum value. However, the absorption cross section uncertainty is a common systematic in the BEST, SAGE, and GALLEX/GNO neutrino source experiments. Here, in this work, we update that cross section, considering a variety of electroweak corrections and the role of transitions to excited states, to establish both a central value and reasonable uncertainty, thereby enabling a more accurate assessment of the statistical significance of the gallium anomalies. Results are given for 51 Cr and 37 Ar sources. The revised neutrino capture rates are used in a reevaluation of the BEST and gallium anomalies.

electroweak interactions in nuclear physics↗
DOE OSTI2024

Supporting ARPA-E Power Grid Optimization (Final Report)

Pacific Northwest National Laboratory (PNNL), Arizona State University (ASU), Georgia Institute of Technology (Georgia Tech), Los Alamos National Laboratory (LANL), National Renewable Energy Laboratory (NREL), Texas A&M University (TAMU), The University of Texas at Austin (UT), and the University of Wisconsin-Madison (UW-M) supported the ARPA-E Grid Optimization (GO) Competition by providing a common problem formulation, data format, datasets, evaluation mechanism, scoring, rules, and results that resulted in the awarding of $\$9.24$ million dollars to teams from academia, industry, and national labs for solving three sets of increasingly difficult non-linear, security- constrained AC Optimal Powerflow (AC-OPF) optimization problems in order to increase the efficiency of the US Electric Grid. It is estimated that a 1% increase in efficiency can save $\$1$ billion. Current industry practices typically use a linear DC model (DC-OPF) in order solve the OPF problem within the time constraints of the operation schedule. The GO Competition challenges the best power engineers, mathematicians, and computer scientists to make possible operational decisions based on accurate physical models. To accomplish this, the GO Competition created a series of Challenges and funded teams to produce the best solver. Challenge 1 was to solve the security constrained Alternating Current Optimal Power Flow (ACOPF) problem. Challenge 2 extended that to by adding adjustable transformer tap ratios, phase shifting transformers, switchable shunts, price-responsive demand, ramp rate constrained generators and loads, and fast-start unit commitment (UC). Furthermore, Challenge 2 was a maximization problem while Challenge 1 was a minimization problem. While Challenge 3 was being developed, the entrants were invited to find better solutions to the Challenge 2 synthetic datasets with no restrictions on time, hardware, or algorithms. The Challenge 2 solutions turned out to be very good. Challenge 3 expanded the Challenge 2 problem further by using multiperiod dynamic markets, including advisory models for extreme weather events, day-ahead markets, and the real-time markets with an extended look-ahead. These problems included active bid-in demand and topology optimization. Together the Challenges used nearly 30 million CPU hours. Since each team was working on the same problem, using the same data, and running on the same hardware, fair comparisons could be drawn as to the best solver. The datasets were varied enough, however, that the best solver for one dataset was not necessarily the best at another, so cumulative scores were used. The process was managed by the PNNL maintained website https://GOCompetition.energy.gov, where Entrants could find information about the problem, the data, the rules, submit their solver for evaluation, and see the scores of all the competing teams on a Leaderboard. Interest was world-wide but only American teams were eligible for prizes. The Competition has produced 34 journal articles 115 papers and been cited over 500 times in the literature, including 12 dissertations (4 from foreign countries; Columbia (2), Germany, and Italy) and 3 from the DOE ExaScale project. Software developed by Pearl Street Technologies for Challenges 1 and 2 is now deployed by Southwest Power Pool (SPP) and Midcontinent Independent Service Operator (MISO). Other teams have received inquiries from venture capitalists. Google DeepMind has thanked the Competition for making the datasets developed for the Competition public. They are using it to train machine learning models. The larger datasets have billions of unknowns to be solved for, but only a small percent matter in the final solution. Knowing what unknowns are important can dramatically speedup the solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1984

Evaluation of criteria for selecting the spectral attributes of digital LANDSAT MSS imagery for discriminating lithological units in the lower Curaca River Valley, Bahia

The use of spectral attributes criteria was investigated, based on measures of statistical distance of separability between thematic classes in MSS digital LANDSAT imagery, in order to select the best subsets of channels in composite colors for the detection and discrimination of lithological units in the lower valley of Curaca River, State of Bahia, Brazil. Three situations were investigated: (1) selection of the three best channels, considering all of the original bands (channels 4, 5, 6, and 7); (2) selection of the three best bands, considering the six MSS band-ratios (channels 4/5, 4/6. 4/7, 5/6, 5/7, and 6/7); and (3) selection of the three best bands in a hybrid approach (the four original bands and the six ratios). A visual analysis was done on color composite images using the selected sets. Results show that the hybrid product (bands 4, 5/7, and 7 with green, blue, and red respectively) and the Normal Color Composite (bands 4, 5, and 7 with blue, green, and red colors respectively) had the best performance.

Paradella, W. R.↗
NASA NTRS2000

Aerospace Systems Design in NASA's Collaborative Engineering Environment

Past designs of complex aerospace systems involved an environment consisting of collocated design teams with project managers, technical discipline experts, and other experts (e.g., manufacturing and systems operation). These experts were generally qualified only on the basis of past design experience and typically had access to a limited set of integrated analysis tools. These environments provided less than desirable design fidelity, often lead to the inability of assessing critical programmatic and technical issues (e.g., cost, risk, technical impacts), and generally derived a design that was not necessarily optimized across the entire system. The continually changing, modern aerospace industry demands systems design processes that involve the best talent available (no matter where it resides) and access to the the best design and analysis tools. A solution to these demands involves a design environment referred to as collaborative engineering. The collaborative engineering environment evolving within the National Aeronautics and Space Administration (NASA) is a capability that enables the Agency's engineering infrastructure to interact and use the best state-of-the-art tools and data across organizational boundaries. Using collaborative engineering, the collocated team is replaced with an interactive team structure where the team members are geographical distributed and the best engineering talent can be applied to the design effort regardless of physical location. In addition, a more efficient, higher quality design product is delivered by bringing together the best engineering talent with more up-to-date design and analysis tools. These tools are focused on interactive, multidisciplinary design and analysis with emphasis on the complete life cycle of the system, and they include nontraditional, integrated tools for life cycle cost estimation and risk assessment. NASA has made substantial progress during the last two years in developing a collaborative engineering environment. NASA is planning to use this collaborative engineering engineering infrastructure to provide better aerospace systems life cycle design and analysis, which includes analytical assessment of the technical and programmatic aspects of a system from "cradle to grave." This paper describes the recent NASA developments in the area of collaborative engineering, the benefits (realized and anticipated) of using the developed capability, and the long-term plans for implementing this capability across Agency.

Monell, Donald W.↗
NASA NTRS1999

Aerospace Systems Design in NASA's Collaborative Engineering Environment

Past designs of complex aerospace systems involved an environment consisting of collocated design teams with project managers, technical discipline experts, and other experts (e.g. manufacturing and systems operations). These experts were generally qualified only on the basis of past design experience and typically had access to a limited set of integrated analysis tools. These environments provided less than desirable design fidelity, often lead to the inability of assessing critical programmatic and technical issues (e.g., cost risk, technical impacts), and generally derived a design that was not necessarily optimized across the entire system. The continually changing, modern aerospace industry demands systems design processes that involve the best talent available (no matter where it resides) and access to the best design and analysis tools. A solution to these demands involves a design environment referred to as collaborative engineering. The collaborative engineering environment evolving within the National Aeronautics and Space Administration (NASA) is a capability that enables the Agency's engineering infrastructure to interact and use the best state-of-the-art tools and data across organizational boundaries. Using collaborative engineering, the collocated team is replaced with an interactive team structure where the team members are geographically distributed and the best engineering talent can be applied to the design effort regardless of physical location. In addition, a more efficient, higher quality design product is delivered by bringing together the best engineering talent with more up-to-date design and analysis tools. These tools are focused on interactive, multidisciplinary design and analysis with emphasis on the complete life cycle of the system, and they include nontraditional, integrated tools for life cycle cost estimation and risk assessment. NASA has made substantial progress during the last two years in developing a collaborative engineering environment. NASA is planning to use this collaborative engineering infrastructure to provide better aerospace systems life cycle design and analysis, which includes analytical assessment of the technical and programmatic aspects of a system from "cradle to grave." This paper describes the recent NASA developments in the area of collaborative engineering, the benefits (realized and anticipated) of using the developed capability, and the long-term plans for implementing this capability across the Agency.

Monell, Donald W.↗
NASA NTRS2004

Adaptive Bio-Inspired Wireless Network Routing for Planetary Surface Exploration

Wireless mobile networks suffer connectivity loss when used in a terrain that has hills, and valleys when line of sight is interrupted or range is exceeded. To resolve this problem and achieve acceptable network performance, we have designed an adaptive, configurable, hybrid system to automatically route network packets along the best path between multiple geographically dispersed modules. This is very useful in planetary surface exploration, especially for ad-hoc mobile networks, where computational devices take an active part in creating a network infrastructure, and can actually be used to route data dynamically and even store data for later transmission between networks. Using inspiration from biological systems, this research proposes to use ant trail algorithms with multi-layered information maps (topographic maps, RF coverage maps) to determine the best route through ad-hoc network at real time. The determination of best route is a complex one, and requires research into the appropriate metrics, best method to identify the best path, optimizing traffic capacity, network performance, reliability, processing capabilities and cost. Real ants are capable of finding the shortest path from their nest to a food source without visual sensing through the use of pheromones. They are also able to adapt to changes in the environment using subtle clues. To use ant trail algorithms, we need to define the probability function. The artificial ant is, in this case, a software agent that moves from node to node on a network graph. The function to calculate the fitness (evaluate the better path) includes: length of the network edge, the coverage index, topology graph index, and pheromone trail left behind by other ant agents. Each agent modifies the environment in two different ways: 1) Local trail updating: As the ant moves between nodes it updates the amount of pheromone on the edge; and 2) Global trail updating: When all ants have completed a tour the ant that found the shortest route updates the edges in its path.

Alena, Richard I.↗
NASA NTRS2013

Range-Specific High-resolution Mesoscale Model Setup

This report summarizes the findings from an AMU task to determine the best model configuration for operational use at the ER and WFF to best predict winds, precipitation, and temperature. The AMU ran test cases in the warm and cool seasons at the ER and for the spring and fall seasons at WFF. For both the ER and WFF, the ARW core outperformed the NMM core. Results for the ER indicate that the Lin microphysical scheme and the YSU PBL scheme is the optimal model configuration for the ER. It consistently produced the best surface and upper air forecasts, while performing fairly well for the precipitation forecasts. Both the Ferrier and Lin microphysical schemes in combination with the YSU PBL scheme performed well for WFF in the spring and fall seasons. The AMU has been tasked with a follow-on modeling effort to recommended local DA and numerical forecast model design optimized for both the ER and WFF to support space launch activities. The AMU will determine the best software and type of assimilation to use, as well as determine the best grid resolution for the initialization based on spatial and temporal availability of data and the wall clock run-time of the initialization. The AMU will transition from the WRF EMS to NU-WRF, a NASA-specific version of the WRF that takes advantage of unique NASA software and datasets. 37

Watson, Leela R.↗
NASA NTRS2013

A Single-column Model Ensemble Approach Applied to the TWP-ICE Experiment

Single-column models (SCM) are useful test beds for investigating the parameterization schemes of numerical weather prediction and climate models. The usefulness of SCM simulations are limited, however, by the accuracy of the best estimate large-scale observations prescribed. Errors estimating the observations will result in uncertainty in modeled simulations. One method to address the modeled uncertainty is to simulate an ensemble where the ensemble members span observational uncertainty. This study first derives an ensemble of large-scale data for the Tropical Warm Pool International Cloud Experiment (TWP-ICE) based on an estimate of a possible source of error in the best estimate product. These data are then used to carry out simulations with 11 SCM and two cloud-resolving models (CRM). Best estimate simulations are also performed. All models show that moisture-related variables are close to observations and there are limited differences between the best estimate and ensemble mean values. The models, however, show different sensitivities to changes in the forcing particularly when weakly forced. The ensemble simulations highlight important differences in the surface evaporation term of the moisture budget between the SCM and CRM. Differences are also apparent between the models in the ensemble mean vertical structure of cloud variables, while for each model, cloud properties are relatively insensitive to forcing. The ensemble is further used to investigate cloud variables and precipitation and identifies differences between CRM and SCM particularly for relationships involving ice. This study highlights the additional analysis that can be performed using ensemble simulations and hence enables a more complete model investigation compared to using the more traditional single best estimate simulation only.

numerical weather forecasting↗
NASA NTRS2019

A Global Climatology of Extratropical Transition Part II: Statistical Performance of the Cyclone Phase Space

This study analyzes the differences between an objective, automated identification of tropical cyclones (TCs) that undergo extratropical transition (ET), and the designation of ET determined subjectively by human forecasters in best-track data in all basins globally. The objective identification of ET is based on the cyclone phase space (CPS), calculated from the Japanese 55-year Reanalysis (JRA-55) or the ECMWF Interim Reanalysis (ERA-Interim). The resulting classification into “ET storms” and “non-ET storms” underlies the global climatology of ET presented in Part I of this study. Here, the authors investigate how well the CPS classifications agree with those in the best-track records calculated from JRA-55 or from ERA-Interim data. According to F1 scores and Matthews correlation coefficients (MCCs), the classification of ET storms in the CPS agrees best with the best-track classification in the Western North Pacific (MCC > 0.7) and the North Atlantic (MCC > 0.5). In other basins, the correlation between the CPS classification and the best-track classification is only slightly higher than that of a random classification. The JRA-55 classification achieves higher performance scores than does the ERA-Interim classification, and the differences are statistically significant in all basins. The lower performance of ERA-Interim is mainly due to a higher false alarm rate, particularly in the Eastern North Pacific. Overall, the results show that while the CPS-based classifications are good enough to be useful for many purposes, there is almost certainly room for improvement – in the representation of the storms in reanalyses, in our objective metrics of ET, and in our scientific understanding of the ET process.

Melanie Bieli↗
NASA NTRSDate not supplied

Gateway Command and Data Handling Network Implementation and Validation

As initial Lunar Gateway modules approach design maturity, the Artemis Network Validation and Integration Lab (ANVIL) has begun demonstrations to validate avionics network dataflows. The flight architecture design includes utilizing Time-Triggered Ethernet (TTE) and layer-3 switching capabilities to enable greater automation and flexibility of critical and best effort traffic. Critical traffic is considered as Time-Triggered (TT), Rate Constrained (RC) and prioritized Best Effort (BE) traffic classes. End systems, such as mission computers, power control, and robotics, use three planes and all traffic classes while other devices interface via Best Effort. Typical best effort devices include video, laptops, wireless access points, and payloads. Other devices have a various hybrid approach of interfaces including alarms, telemetry/logging, and communication units. In the paper, we will present an update of the Gateway network architecture and how the system will operate nominally and during a stack topology reconfiguration. We will also discuss the network risks, and trade-offs of performance, flexibility, and redundancy. Finally, we will show the process for validation, demonstration, and verification approaches to the vehicle network.

Gateway↗
DOE OSTI2022

Degradation Reduces Microbial Richness and Alters Microbial Functions in an Australian Peatland

Abstract Peatland ecosystems cover only 3% of the world’s land area; however, they store one-third of the global soil carbon (C). Microbial communities are the main drivers of C decomposition in peatlands, yet we have limited knowledge of their structure and function. While the microbial communities in the Northern Hemisphere peatlands are well documented, we have limited understanding of microbial community composition and function in the Southern Hemisphere peatlands, especially in Australia. We investigated the vertical stratification of prokaryote and fungal communities from Wellington Plains peatland in the Australian Alps. Within the peatland complex, bog peat was sampled from the intact peatland and dried peat from the degraded peatland along a vertical soil depth gradient (i.e., acrotelm, mesotelm, and catotelm). We analyzed the prokaryote and fungal community structure, predicted functional profiles of prokaryotes using PICRUSt, and assigned soil fungal guilds using FUNGuild. We found that the structure and function of prokaryotes were vertically stratified in the intact bog. Soil carbon, manganese, nitrogen, lead, and sodium content best explained the prokaryote composition. Prokaryote richness was significantly higher in the intact bog acrotelm compared to degraded bog acrotelm. Fungal composition remained similar across the soil depth gradient; however, there was a considerable increase in saprotroph abundance and decrease in endophyte abundance along the vertical soil depth gradient. The abundance of saprotrophs and plant pathogens was two-fold higher in the degraded bog acrotelm. Soil manganese and nitrogen content, electrical conductivity, and water table level (cm) best explained the fungal composition. Our results demonstrate that both fungal and prokaryote communities are shaped by soil abiotic factors and that peatland degradation reduces microbial richness and alters microbial functions. Thus, current and future changes to the environmental conditions in these peatlands may lead to altered microbial community structures and associated functions which may have implications for broader ecosystem function changes in peatlands.

Environmental Sciences & Ecology↗
DOE OSTI2021

Environment-sensitivity functions for gross primary productivity in light use efficiency models

The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO 2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO 2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Chemometric modeling of power conversion efficiency of organic dyes in dye sensitized solar cells for the future renewable energy

The present study reports chemometric modeling of power conversion efficiency (PCE) of dye sensitized solar cells (DSSCs) using the biggest available data set till date which comprises around 1200 dyes covering 7 chemical classes. To extract the best structural features required for higher PCE, we have developed multiple partial least squares (PLS) quantitative structure-property relationship (QSPR) models for the Triphenylamine, Phenothiazine, Indoline, Porphyrin, Coumarin, Carbazole and Diphenylamine chemical classes using descriptors derived from the best subset selection method followed by selection of best five models in each dataset based on the Mean Absolute Error (MAE) values. The models were validated both internally and externally followed by the consensus predictions employing “Intelligent Consensus Predictor” tool to examine whether the quality of predictions can be improved with the “intelligent” selection of multiple PLS models. The quality of predictions for the respective external sets showed that the consensus models (CM) are better than the individual models (IM) in most of the cases. From the insights of the developed models, we concluded that attributes like a packed structure toward higher conductivity of electrons, auxiliary donor fragment of aromatic tertiary amines, number of thiophenes inducing the bathochromic shift and augmenting the absorption, presence of additional electron donors, enhancement of electron-donating abilities, number of non-aromatic conjugated C(sp 2 ) which helps as conjugation extension units to broaden the absorption and highly conjugated $π$-systems exert positive contributions to the PCE. On the contrary, features negatively contributing to PCE are the followings: fragments which lower the tendency of localized $π–π*$ transition, fragments related to larger volume and surface area of dyes along with hydrophobicity resulting in poor adhesion, fragment RC = N causing dye hydrolysis, steric hindrance for π electronic mobility, fragments enhancing polarity, etc. Finally, the identified features from the best QSPR model of the coumarin dataset was employed in designing of ten more efficient coumarin dyes (predicted %PCE ranging from 8.93 to 10.62) than the existing ones.

14 SOLAR ENERGY↗
DOE OSTI2023

Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes

Spin-crossover (SCO) complexes are materials that exhibit changes in the spin state in response to external stimuli, with potential applications in molecular electronics. It is challenging to know a priori how to design ligands to achieve the delicate balance of entropic and enthalpic contributions needed to tailor a transition temperature close to room temperature. Here, we leverage the SCO complexes from the previously curated SCO-95 data set [Vennelakanti et al. J. Chem. Phys. 159, 024120 (2023)] to train three machine learning (ML) models for transition temperature (T 1/2 ) prediction using graph-based revised autocorrelations as features. We perform feature selection using random forest-ranked recursive feature addition (RF-RFA) to identify the features essential to model transferability. Of the ML models considered, the full feature set RF and recursive feature addition RF models perform best, achieving moderate correlation to experimental T 1/2 values. We then compare ML T 1/2 predictions to those from three previously identified best-performing density functional approximations (DFAs) which accurately predict SCO behavior across SCO-95, finding that the ML models predict T 1/2 more accurately than the best-performing DFAs. In addition, we study ML model predictions for a set of 18 SCO complexes for which only estimated T 1/2 values are available. Upon excluding outliers from this set, the RF-RFA RF model shows a strong correlation to estimated T 1/2 values with a Pearson’s r of 0.82. In contrast, DFA-predicted T 1/2 values have large errors and show no correlation to estimated T 1/2 values over the same set of complexes. Overall, our study demonstrates slightly superior performance of ML models in comparison with some of the best-performing DFAs, and we expect ML models to improve further as larger data sets of SCO complexes are curated and become available for model training.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗